Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.
Motion planning and control are critical operations in autonomous driving. Changing lane is a fundamental function of an autonomous vehicle (also referred to as an autonomous driving vehicle or ADV) to avoid obstacles and to improve a trip time-efficiency. However, making the lane-changing safe and efficient is a difficult task because instead of computing the driving conditions of a current lane, an autonomous vehicle needs to consider the driving conditions of both the current lane and a target lane. In addition, a transition path between the current lane and the target lane is unknown and dynamically changing, which adds the complexity of decision making. Further, the state of lane-changing needs to be determined continuously during the process, which requires a high accuracy of localization and complex logic between states.